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Article

What Influences Users’ Intention to Share Works in Designer-Driven User-Generated Content Communities? A Study Based on Self-Determination Theory

by
Hongcai Song
,
Jie Wei
and
Qianling Jiang
*
School of Design, Jiangnan University, Wuxi 214122, China
*
Author to whom correspondence should be addressed.
Systems 2023, 11(11), 540; https://doi.org/10.3390/systems11110540
Submission received: 25 September 2023 / Revised: 1 November 2023 / Accepted: 3 November 2023 / Published: 6 November 2023
(This article belongs to the Special Issue Communication for the Digital Media Age)
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Versions Notes

Abstract

:
Designer UGC (user-generated content) communities serve as the epicenter of contemporary innovation and creativity, offering a platform for a broad design community to showcase their talents, communicate, and collaborate. Grounded in Self-Determination Theory, this study constructs a research model aiming to delve deeply into the underlying driving factors influencing users’ intention to share their works within these communities. Through online surveys targeting UGC community users and employing structural equation modeling, this research validates the determinants affecting their sharing intentions and dissects the pathways of each influencing factor. The findings reveal that in designer UGC communities, factors such as autonomy, competence, relatedness, online social support, and value fit have a significant positive impact on users’ intention to share their works. This study offers profound insights into the intrinsic logic behind user behaviors in the design creativity domain, also providing robust guidance for the operation and management of online community platforms.

1. Introduction

In the digital era, design creativity is no longer an isolated individual activity but is immersed in an extensive virtual social environment. Compared to the real world, communication in social networks is unhindered by time and space constraints, allowing users to remain connected with family, friends, colleagues, and even strangers at any time and from anywhere. The intersection between virtual social interactions and the real world is ever intensifying. Virtual environments endow creativity with unparalleled interactive and dissemination capabilities, and globally, social media has emerged as the most prevalent communication tool. Various social media platforms offer forums for discussions on a myriad of topics—from politics, economics, and environment to work [1,2,3,4], thereby promoting knowledge dissemination. With the incessant evolution and innovation of technology, new social networks rapidly emerge to meet the growing demands of users [5,6]. In this interconnected digital ecosystem full of opportunities and challenges, social networks are constructed as an optimal system [7,8], purposed for world exploration, knowledge creation, and deep insights into both traditional and novel themes [9]. Online social networks have indeed become the primary channels for people to express opinions, participate in discussions, and access information [10,11].
The concept of UGC (user-generated content) originated from the evolution of social media, referring to innovative texts, images, audio, or video contents created by non-professional users [12]. The UGC community is open to a wide range of people, and its content is diverse, with different quality and professionalism. A defining characteristic of social media platforms is their pronounced interactivity and sociability. This allows users to efficiently create and share diverse UGC, which not only represents content but also serves as a profound medium of social interaction, influencing users’ attitudes and behaviors in various aspects [13,14]. The generation of user content is intricately linked to the complex structure of social networks. By deeply mining the information provided by social media, profound insights can be identified, fostering the birth of new knowledge [15]. This process not only aids in the formation and dissemination of knowledge but also significantly affects user behavior and decision-making within social media. UGC has shifted users’ internet habits from primarily downloading to a balanced emphasis on both downloading and uploading. Users have now evolved from being mere consumers of online content to becoming its creators. In our study, we define users as individuals active in UGC communities who browse, comment, and interact with content but may not necessarily initiate sharing. Sharers refer to individuals who proactively share their own or others’ content within the community, demonstrating higher engagement. Sharees are individuals who receive shared content, accepting others’ shares or suggestions, but may not actively engage in content creation or sharing themselves. This research aims to understand the driving factors behind users’ sharing behavior, with the goal of converting more users into sharers [16].
Over the past few decades, extensive research on UGC and its impacts has been conducted [17,18,19,20,21,22,23,24], which can be categorized into three main areas. Firstly, a number of studies have focused on identifying the factors that influence UGC, as the sustained development of online platforms largely relies on consistent contributions from users. Research has revealed that factors such as social capital [25,26,27,28,29], audience size [30], financial incentives [29,31,32,33], identity disclosure [20,34], and the integration of platforms with other social media services play significant roles in determining UGC creation. Secondly, some studies have delved into the characteristics of UGC, such as output, emotional tendencies, and linguistic features [35,36]. Lastly, other research endeavors have examined the impact of UGC on consumer decision-making [37,38,39,40], product sales [41,42], and business competition [42,43,44]. Our study primarily resides within the first area, aiming to provide fresh insights and contributions by deeply understanding the factors influencing users’ intention to share their works. While there have been some studies on UGC communities, research on designer-driven UGC communities remains limited, hence making this study both challenging and innovative.
In terms of research methodologies, scholars have provided a variety of tools and methods to explore the different directions of UGC. Some studies employ semantic and sentiment analysis to extract meaningful information from UGC [22]. Multidisciplinary literature review is also a common approach, aiming to consolidate and analyze academic papers on UGC, seeking to establish a broader definition of UGC [21]. Other research adopts a phenomenological interpretative perspective, using in-depth interviews and qualitative analysis to understand the constructions and relationships within UGC [45]. Additionally, some studies utilize multi-state survival analysis to explore user retention, switching, multi-platform behavior patterns, and underlying drivers on online platforms. This method offers a dynamic viewpoint, aiding in understanding the evolution of user behaviors across different social media platforms [46]. This research gathers data through questionnaire surveys to support the research questions and hypotheses. Through the questionnaire, researchers can obtain information on respondents’ views, attitudes, and behaviors towards UGC, allowing for quantitative analysis and interpretation of research questions.
Compared to traditional UGC communities, designer-driven UGC communities are more oriented towards attracting professional designers or specialists in related fields. Users typically possess a high degree of expertise and skills, with their content primarily delving into specialized areas like design, creativity, and art. The UGC community closely integrates content and sharing into the social media platform. This sharing is mainly driven by the user’s inner motivation, and the user’s intrinsic sharing motivation plays a key role, including the need for individual achievement [24,47,48]. However, given the voluntary nature of user contributions, maintaining long-term user engagement in UGC communities remains a significant challenge [46,48]. As such, UGC communities often grapple with content shortages. In terms of designer-driven UGC communities, the active participation and sharing by users are of great importance to their sustainable development. Both current research and practices regard sustainability as the core driver of innovative activities [49,50]. The sharing of user works decides the content quality, diversity, and appeal of UGC communities, which are crucial for sustainable community growth. Enhancing the intention to share works can infuse the community with valuable content, attracting more users and creating a virtuous cycle. Furthermore, this study offers targeted strategies and managerial suggestions for designer-driven UGC communities. A deeper understanding of these factors will aid community platforms in better catering to user needs, stimulating their sharing intentions, and fostering continuous development in the realm of social innovation.

2. Theoretical Background and Research Hypotheses

This study adopts Self-Determination Theory as its theoretical foundation and employs online social support, value fit, and tie strength as mediating constructs to delve deeply into the intrinsic motivations behind designers’ intention to share their works in designer-driven UGC communities. In Table 1, we provide detailed explanations for these constructs, aiming to present the research logic more lucidly.

2.1. Self-Determination Theory

Self-Determination Theory (SDT) originated in the 1980s and was proposed by American psychologists Deci and Ryan; it explores the driving mechanisms of individual behavior from a motivational perspective, focusing on the degree of autonomy and self-determination exhibited in human actions [51]. A sub-theory of SDT, known as the Basic Psychological Needs Theory, posits that humans have three fundamental psychological needs: autonomy, competence, and relatedness. Herein, autonomy pertains to an individual’s belief in their ability to autonomously determine actions. When individuals are given the right of choice or autonomy, their intrinsic motivation increases [52,53,54]. Competence encompasses an individual’s proactive exploration of the external environment, cultivating confidence in their ability to manage said environment. Competence is related to task-mastering and experience [55,56]. Relatedness refers to the emotional experiences an individual feels in social and interpersonal relationships, such as a sense of belonging, identification, and acceptance [57]. Furthermore, this theory has been extensively applied to social media research to explain individuals’ behavioral motivations and intentions in online environments. These three needs are the basis of intrinsic motivation and self-determination. When they are met, they can guide positive results in various areas of life [52,53,58].
Self-Determination Theory is a widely recognized psychological framework that has seen extensive application across various fields, including the study of social media and online communities. SDT offers a robust theoretical lens for understanding individual behavioral motivations within the context of social media environments [59,60]. SDT emphasizes the degree to which an individual’s psychological needs are met. Sharing works and engaging on social media platforms are often associated with fulfilling individuals’ needs for autonomy, competence, and relatedness, making SDT highly applicable in elucidating such behaviors. Furthermore, this study also selects factors related to SDT, such as value fit, tie strength, and online social support, as these are intricately associated with users’ intentions to share their works within designer-driven UGC communities. These elements influence users’ intrinsic motivations, subsequently impacting designers’ intentions to share their creations.

2.2. Self-Determination and Work-Sharing Intentions

The research results of [61] indicated a positive correlation between feelings of autonomy, competence, and belongingness on social media and the willingness to share works, which can be attributed to social environments fostering the satisfaction of individual needs for autonomy, competence, and relatedness [62]. Martine and colleagues found in their research on employee knowledge-sharing behaviors that satisfying an individual’s need for autonomy can stimulate their intention to share [63,64]. Belanger and others employed a natural experimental method to examine the determinants of shared bicycle use cognition. Their results revealed a connection between an increase in sharing intention and an increase in individual competence [65]. Additionally, Ref. [66], through their investigation into fostering a sense of belonging in virtual communities, discovered that members’ feelings of relatedness positively correlated with their intention to acquire and share knowledge, further confirming the role of relatedness in promoting knowledge-sharing intention. According to Self-Determination Theory, when these psychological needs of individuals are satisfied, they are more likely to exhibit proactive behavioral intentions. Hence, the following hypotheses are proposed based on the above discussion:
Hypothesis 1 (H1). 
Autonomy has a significant positive impact on users’ work-sharing intention in designer-driven UGC communities.
Hypothesis 2 (H2). 
Competence has a significant positive impact on users’ work-sharing intention in designer-driven UGC communities.
Hypothesis 3 (H3). 
Relatedness has a significant positive impact on users’ work-sharing intention in designer-driven UGC communities.

2.3. Online Social Support

Online social support refers to obtaining information support and emotional support from others through social media platforms, such as attention, recognition and encouragement [67]. Ref. [68] argued that online social support tends to have a more significant impact than actual support. As a way of computer media, network communication has been proved to help promote interaction between users [69]. Through online social networking, individuals can not only gain richer emotional support but also maintain a more comfortable emotional distance [70]. The social characteristics of social networks make individuals more willing to share positive valence information related to themselves, because it is closely related to individual’s social attraction, maintaining social relationships and obtaining social support [71,72,73]. Based on the framework of SDT, it is confirmed that there is a positive correlation between online social support and sense of autonomy, competence and relatedness. Information support and emotional support have a significant impact on users’ autonomous participation behavior in online interactive platforms [74]. In addition, Ref. [75] found through qualitative research that participants in social games generally believe that they can obtain resources and emotional support from other members of the team. The positive impact of these social supports enhances the sense of relatedness of game participants and deepens their desire to connect with others. This kind of online social capital not only plays a role in the virtual environment but also significantly enhances the emotional connection and social belonging in real life [76]. On this basis, this study proposes the following hypotheses:
Hypothesis 4 (H4). 
Online social support has a positive impact on designers’ sense of autonomy.
Hypothesis 5 (H5). 
Online social support has a positive impact on designers’ sense of competence.
Hypothesis 6 (H6). 
Online social support has a positive impact on designers’ sense of relatedness.

2.4. Value Fit

Value fit, also known as value congruence, refers to the degree of alignment between an individual’s core values and the values of their organization [77,78]. The essence of a social platform is built on homologous values rather than homologous status [79]. When individuals perceive that their inherent values align with the values of their surrounding environment or interpersonal relationships, they often experience a heightened sense of self-determination. Hence, they are more willing and likely to post information on social media platforms that aligns with their beliefs or values [80]. Individuals might enhance their self-identity and self-efficacy by sharing information that conforms to social norms and anticipate social recognition. In other words, value fit is considered an element that aids in enhancing the sense of self-determination, assisting individuals in building more intrinsically consistent and internally driven behavioral patterns. This uplifts their sense of autonomy and relatedness in their personal behaviors, subsequently influencing their sharing behaviors. Scholz and others have proposed that sharing behavior is a manifestation of value-based decision-making [81]. Based on this, the study proposes the following hypotheses:
Hypothesis 7 (H7). 
Value fit positively affects the sense of autonomy.
Hypothesis 8 (H8). 
Value fit positively affects the sense of competence.
Hypothesis 9 (H9). 
Value fit positively affects the sense of relatedness.
Hypothesis 10 (H10). 
Online social support positively influences value fit.

2.5. Tie Strength

In the designer UGC community, tie strength refers to the closeness and frequency of interactions between individuals and others. Online social support can enhance tie strength by offering emotional support, sharing valuable information, and establishing deeper interactions. Existing studies have empirically proven that social support can affect member willingness, encouraging members to communicate again using existing interaction platforms [82]. The perception of social support is related to intimate relationships [83]. The establishment of such relationships usually require repeated interactions [84]. The social interaction behavior of online community users affects users’ sense of relatedness in the community, their trust level towards other users, and ultimately affects user community usage behavior [85]. Jang and others examined user self-presentation on Facebook, and the experimental results showed that the tie strength between the sharer and the sharee significantly affects the sharer’s self-presentation style [86]. Therefore, this study proposes the following research hypotheses:
Hypothesis 11 (H11). 
Online social support positively affects tie strength.
Hypothesis 12 (H12). 
Value fit positively affects tie strength.
Hypothesis 13 (H13). 
Tie strength positively affects the work-sharing intention.
To sum up, this study constructs a model that explains the influencing factors of the willingness of users within the designer UGC communities to share their works, as shown in Figure 1.

3. Methodology

3.1. Research Framework

In this study, questionnaire design serves as a key component since it directly impacts our comprehensive understanding of the user intention within designer UGC communities. The research process primarily comprises four main stages: questionnaire design, sample selection, data collection, and data analysis.
In the first stage, the questionnaire is meticulous planned to encompass elements from Self-Determination Theory (autonomy, competence, and relatedness), online social support, value fit, tie strength, and work sharing intention. The questionnaire consists of multiple sections, each focusing on a specific theme to better gather information on user intentions. Prior to its official distribution, the study incorporated a pilot test and adjusted the questionnaire based on participants’ feedback to guarantee the reliability of the questionnaire content.
During the second stage, we screened and catalogued participant identities. All selected respondents are designers who have either remained active within or have shared their works on designer-driven UGC communities.
In the third stage, we disseminated the questionnaire to respondents via an online survey platform to collect data. Respondents completed the survey based on their experiences and attitudes towards the designer-driven UGC communities.
In the final stage, we employed IBM SPSS and Amos 23 for a comprehensive and accurate data analysis. Conclusions and recommendations were drawn based on the data analysis and discussion results.

3.2. Questionnaire Design

Considering the driving factors of the designer’s intentions to share works in the UGC community, the measurement items of this study were set and optimized according to the relevant research results, and a multi-dimensional questionnaire survey was designed. The questionnaire covers multiple constructs such as value fit, tie strength, online social support, autonomy, competence, relatedness, and work-sharing intention, as well as the basic information of the respondents. After the draft of the questionnaire was completed, a pre-test was conducted, and two experts in related fields were consulted. According to their feedback, explanation of the relevant concepts was added and the language expression corrected. The following Table 2 presents the measurement items of the research variables of the users’ intentions to share works in the designer-driven UGC community.

3.3. Sampling

In order to ensure the credibility and representativeness of the research, we first recruited through China’s online designer community, and the selected participants maintained a continuous active or shared work in the designer UGC community. Participants had a large age span and covered a wide range of professional backgrounds, including but not limited to visual communication design, industrial design, product design, environmental design, clothing design, digital media design and public art design. The diversity of samples provided a sufficient basis for this study. The demographic information of the respondents are shown in Table 3.

3.4. Data Collection

The questionnaire was collected from July to August 2023 through an online survey platform and a total of 326 valid samples were collected in the formal survey stage. After excluding samples with low-quality responses (short duration or consistent answers across all scale items), 301 high-quality valid questionnaires were finally obtained, with an effective recovery rate of 92.33%, fully meeting the research needs. The questionnaire was filled out in the environment of the respondents’ own choice, including home, company, etc. We ensured the confidentiality of respondents’ personal information and answers, and obtained their consent to participate in the survey.

4. Data Analysis

Data analysis was a critical component of this study, employed to validate research hypotheses and evaluate model fit. It primarily consisted of three steps: reliability test, confirmatory factor analysis (CFA), and model fit test. A reliability test was conducted using IBM SPSS software, wherein each construct within the structural equation was individually analyzed to ensure the data possessed an acceptable level of reliability, paving the way for further analysis. CFA was executed using IBM AMOS 23.0 software to ensure that the constructs had satisfactory convergent and discriminant validity, thereby providing a robust data foundation for subsequent analytical endeavors. A model fit test was conducted through IBM AMOS 23.0 to test the hypothesized model with 5000 bootstrap samples, aiming to evaluate the fit level between the proposed model and the data. Lastly, the structural equation modeling (SEM) approach was applied to analyze the paths and confirm the results of the hypothesis tests. Detailed steps can be found in Figure 2.

4.1. Reliability Test

The results of the reliability test showed that the overall Cronbach’s alpha coefficient for the questionnaire was 0.981. Cronbach’s alpha coefficients for value fit, tie strength, online social support, autonomy, competence, relatedness, and work-sharing intention scales were 0.912, 0.917, 0.936, 0.947, 0.935, 0.949, and 0.930, respectively. Moreover, the corrected item-to-total (CITC) values for all analysis items were greater than 0.7, indicating a good correlation between the items and the overall scale. In summary, the reliability of the data was of high, making them suitable for further analysis.

4.2. Confirmatory Factor Analysis

In the analysis process, this study assessed factor loadings, composite reliability (CR) values, and average variance extracted (AVE) values. If both the factor loadings and CR values are greater than 0.7 [94], and the AVE value exceeds 0.5 [95], the measurement scale is deemed to possess good convergent validity. The results (as shown in Table 4) indicated that the loadings and CR values for all factors surpassed 0.9, and the AVE values also exceeded 0.7, demonstrating that the relevant constructs involved exhibited strong convergent validity.
As shown in Table 5, the square root of the AVE value of each construct (the value on the diagonal) was greater than the correlation coefficients between them and other constructs. This result showed that the scale used in this study had good discriminant validity between different constructs.

4.3. Model Fit Test

Scholars such as Schumacker and Lomax [96] and Kline [97] have suggested that multiple fit indices should be considered collectively when evaluating model fit, rather than relying solely on the p-value. The results of the model fit test are presented in Table 6. This research found that the fit indices for the model, including the chi-square to degrees of freedom ratio (χ2/df), RMSEA, RMR, GFI, NFI, NNFI, TLI, IFI, and SRMR, were all within the ideal range [98,99], indicating that the model proposed in this study demonstrated a good fit.
This study employed structural equation modeling for path analysis to further test the proposed hypotheses. The results of the path analysis are presented in Table 7 and Figure 3. Of the 13 hypotheses posited in this research, 10 were supported, while 3 were not.

5. Discussion

This study aimed to explore the deep-seated motivating factors behind users’ intention to share their works within designer UGC communities. From the perspective of Self-Determination Theory, we applied structural equation modeling for analysis. Through an integrated approach involving a literature review, questionnaire surveys, and data analysis, we unveiled the complex psychological drivers of the sharing intentions in designer UGC communities, influenced by individuals’ sense of autonomy, competence, relatedness, online social support, tie strength, and value fit. The following content provides a comprehensive discussion of the research findings, while also highlighting the study’s limitations and suggesting directions for future research.

5.1. Factors Influencing Sharing Intention

The results of standardized path coefficients indicate that the sense of autonomy (H1: 0.269), competence (H2: 0.178), and relatedness (H3: 0.518) have a significant positive impact on the work-sharing intention. This further validates the applicability of Self-Determination Theory in virtual social settings and reveals the close relationship between the fulfillment of psychological needs and sharing behaviors. Self-affirmation of creative abilities lies at the heart of innovation [100]. For designers, sharing their works in UGC communities can be seen as a means of self-presentation and communication. The fulfillment of the sense of autonomy, competence, and relatedness might be pivotal motivators driving their sharing behaviors. Based on the Basic Psychological Needs Theory within Self-Determination Theory, when the psychological needs of autonomy, competence, and relatedness are met, designers progressively tap into deeper intrinsic motivations until they are propelled into action [101]. These findings offer practical guidance for managers of designer UGC communities: by promoting more autonomy, fostering activities that encourage a sense of competence, and nurturing a sense of community relatedness, they can enhance designers’ intention to share their works.
Although online social support did not directly impact the sharing intention, it has a positive influence on shaping a sense of self-determination, as reflected by online social support coefficients (H4: 0.717, H5: 0.842, H6: 0.854). This result can be attributed to the interplay of multiple factors. Social interaction, social support, and social capital, or the sense of social fulfillment, are primary motivations for people to use social media [102]. These factors also influence individual self-expression on social media platforms [103]. Active interactions and social support for designers within UGC communities cannot only promote their psychological state but might also strengthen their social relationships and sense of relatedness. This emphasizes the vital role of social interactions within designer UGC communities in affecting designers’ intentions and behaviors. Platform managers can enhance designers’ social support experiences, thereby boosting their intention to share their works, by encouraging interactions among users and offering support and incentives.
The value fit also plays a role in influencing users’ work-sharing intention. The study results indicate that value fit positively impacts the sense of autonomy (H7: 0.241). However, its influence on the feelings of competence and relatedness is not significant. Value fit positively impacts the sense of autonomy by fulfilling basic psychological needs and reinforcing internal consistency. Byrne’s similarity-attraction paradigm posits that affirmation of one’s viewpoints from external sources is a fundamental need for individuals, and the level of fulfillment of this need greatly affects them. Achieving value fit is a way to satisfy this need of individuals [104]. By interacting with those who are similar to oneself (or sharing similar values), individuals can achieve validation of their viewpoints [105]. Therefore, when an individual’s core values align with the platform’s consensus, it creates a resonant environment for designers, encouraging them to genuinely express themselves, thus enhancing their sense of autonomy. Concurrently, behaviors in alignment with personal values stimulate users’ intrinsic motivations, reducing external pressures. This alignment and intrinsic motivation together promote a sense of community relatedness and autonomy, laying the foundation for the positive influence of value fit.
Online social support has a significant positive impact on value fit (H10: 0.875). Online social support creates a positive communication atmosphere for users, promoting the free expression of views and values. This helps to understand and facilitate dialogue between different value systems, thereby strengthening the experience of value fit. At the same time, online social support also provides opportunities to access information and different perspectives, helping individuals better understand the community’s consensus and values. This reinforces cognitive consistency, thereby enhancing a sense of community identification. Furthermore, online social support gives individuals a sense of social recognition, especially when their views align with the mainstream values of the community. This strengthens individuals’ confidence in their viewpoints, thereby enhancing the experience of value fit.
Online social support has a significant positive impact on tie strength (H11: 0.800). Firstly, it increases the opportunities for designers to connect with one another. Through online communities, designers can establish connections with peers from various locations, expanding their social circles and offering more opportunities to build relationships. Secondly, a good online community atmosphere can enhance information sharing and interaction among designers. This means they are more inclined to share experiences, insights, and ideas, deepening their mutual understanding and further strengthening their connections. Thirdly, one of the characteristics of online social interaction is that it facilitates continuous interaction. Without constraints of geographical location or time, designers can communicate with their peers whenever necessary, which helps to maintain the vibrancy and depth of their relationships. Moreover, compared to face-to-face interactions, online socializing offers a relatively low-pressure environment. This makes it easier for individuals to overcome social anxiety, allowing them to more comfortably establish connections and interact with others.
Value fit has a significant positive impact on tie strength (H12: 0.178). A high degree of value fit can evoke resonance among designers. When an individual’s core values align with those of others or an organization, the sense of closeness and relevance is enhanced. Furthermore, value fit helps establish trust and identification. Similar values make it easier for individuals to trust one another, accept each other’s viewpoints and actions, thereby increasing the stability and intimacy of the relationship. Value fit can also reduce conflicts and frictions, maintaining a harmonious relationship. Finally, when an individual’s core values are recognized and respected in a relationship, the sense of fulfillment intensifies, contributing to the improvement of the relationship’s quality and depth.

5.2. Explanation of Insignificant Relationship Paths

Some path hypotheses were not supported. For instance, Hypothesis 8 (H8: 0.091), Hypothesis 9 (H8: 0.080), and Hypothesis 13 (H8: 0.043) did not obtain significant path coefficients, namely the effects of value fit on competence and relatedness, as well as the influence of tie strength on the work-sharing intention. This might stem from the unique nature of online UGC communities, leading to some changes in the theory when applied to this context. There could also be other factors not considered that affect these path relationships, such as sample characteristics, research background, etc. Further research is required for a deeper exploration.
The hypotheses regarding the effects of value fit on competence and relatedness were not supported. Analysis in this study reveals that when an individual’s core values are not aligned with the mainstream environment, cognitive dissonance and feelings of unease would arise. This may lead the individual to feel distanced from the social group, making it challenging to establish close social ties, subsequently diminishing their experiences of competence and relatedness. As for the hypothesis concerning the influence of tie strength on the work-sharing intention, the analysis results indicate that some designers might be more sensitive to issues of privacy and confidentiality. They may worry that sharing their works on interactive designer platforms exposes them to risks related to personal information or idea leaks. The subjective nature of privacy issues, the widespread expansion of online platforms, multi-party participation, the nature of data disclosure, and associated ethics make determining the contradictory effects between personal privacy issues and behavior challenging [106,107]. Furthermore, some designers might avoid sharing due to concerns about others’ evaluations and criticisms, aiming to sidestep potential negative consequences triggered by adverse feedback, such as disputes or misunderstandings.
In designer UGC communities, sporadic content sharing might diminish the creator’s sense of presence and make it challenging to establish meaningful relationships with other designers, consequently reducing users’ sharing behavior [108,109]. Similarly, excessive content sharing can also harm users’ sharing behaviors. Firstly, it might lead to boredom and user fatigue as opportunities for users to learn or entertain from the sharer’s content diminish [110,111]. Due to resource constraints, prolific creators might be compelled to create and share bland content lacking creativity [112,113], intensifying user fatigue and prompting opposition other than sharing behaviors. Therefore, in the management and operation of designer UGC communities, careful consideration is needed on how to balance the quantity and quality of shared content to maximize users’ intention to share and active participation.

6. Conclusions and Future Research

6.1. Conclusions

When comprehensively considering the relationship between factors such as SDT, online social support, value fit, and tie strength and designers’ work-sharing intention, the following conclusions can be drawn: users’ work-sharing intention within designer-driven UGC communities is influenced by various factors. The factors derived from Self-Determination Theory can directly inspire designers with a proactive desire to share. Online social support can shape a sense of self-determination, thereby enhancing designers’ intention to share. Value fit can enhance users’ autonomy, strengthening designers’ identity with the UGC community environment. However, the tie strength triggers negative emotions in some designers, raising concerns about potential personal information leaks and ideas being plagiarized, which then act as constraints on the work-sharing intentions. These factors form intricate interactions among cognition, emotion, and behavior, together influencing the designers’ final decisions.

6.2. Theoretical Contribution

Our research validates and extends the application of Self-Determination Theory within the design discipline. While SDT has been widely implemented across various disciplines, its exploration within creative and design contexts remains relatively limited. By correlating the central concepts of SDT with behaviors of design professionals, this study has bolstered the theory’s applicability, laying a solid groundwork for its utilization in innovation and creative fields. Additionally, our findings indicate that within designer-driven UGC communities, factors such as autonomy, competence, relatedness, online social support, and value fit all maintain a positive correlation with users’ work-sharing intention. These insights provide further understanding of autonomy, self-determination, and users’ intrinsic motivations within SDT, offering empirical backing to the theory’s ongoing evolution.

6.3. Practical Contribution

From a managerial perspective, the results of this study offer practical guidance for administrators of designer UGC communities. Firstly, in meeting the actual needs of designers, platform administrators can consider enrich users’ community participation styles, thereby fulfilling designers’ needs for autonomy. Additionally, by organizing creative contests, launching themed events, etc., they can encourage designers to participate and showcase their creative talents, which can further satisfy their competence needs. Moreover, community maintenance should be a focus for platform managers. They should strive to create a positive social atmosphere. By hosting online events and providing interactive forums, they can foster communication and identification among designers, fulfilling their need for relatedness. Furthermore, online social support should also be a priority for platform administrators. Platform administrators can guide users to engage in interactive behaviors such as commenting, liking, and sharing to foster positive social feedback and support. Designer-driven UGC communities can also utilize social functions to encourage designers to establish closer connections with other users, promoting the enhancement of tie strength.

6.4. Limitations and Future Research

While our study endeavored to provide comprehensive and in-depth insights, certain limitations remain. Firstly, our research sample predominantly consists of users from designer-driven UGC communities in China. The regional and cultural characteristics of the sample might influence the generalizability of the findings. Secondly, the study employed a cross-sectional design, which hampers our ability to establish causal relationships. The questionnaire-based approach might be subject to recall bias and subjective evaluations, especially when addressing subjective intentions and emotions. Future research might benefit from incorporating a diverse set of data-collection methods, such as face-to-face interviews, observational studies, or biometric data, to glean more holistic and objective information.
Future studies could employ a more in-depth longitudinal research design to better understand the impact of time on users’ work-sharing intention within designer-driven UGC communities, helping to reveal long-term trends and causal relationships. To enhance the generalizability of the research, future studies might consider selecting samples from multiple countries and diverse cultural backgrounds to explore the similarities and differences in designer-driven UGC communities across varying cultural contexts, furthering our understanding of the behaviors of designers globally. Future research can introduce a wider variety of data-collection methods, such as in-depth interviews and observational studies, to achieve more comprehensive data collection, thereby more accurately unveiling designers’ intrinsic motivations and emotions. Future studies can also broaden their scope, delving deeper into aspects like designers’ technology acceptance, personal traits, types of creativity, and activity levels within online social networks, providing more insights into the underlying mechanisms influenced by these multifaceted factors.
In summary, this study delves deeply into the intention of designers to share their works in UGC communities, analyzing the complex cognitive, emotional, and behavioral elements behind it. This not only offers valuable insights and guidance for building and optimizing designer UGC communities but also provides important directions for future research. Future studies will further deepen our understanding of these influencing mechanisms to encourage continuous sharing by designers in UGC communities, promoting the development of innovation and creative works.

Author Contributions

Conceptualization, J.W. and Q.J.; methodology, Q.J.; formal analysis, Q.J.; investigation, H.S.; data curation, H.S.; writing—original draft preparation, H.S.; writing—review and editing, J.W. and Q.J.; visualization, H.S.; supervision, J.W. and Q.J.; and project administration, J.W. and Q.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in this study.

Data Availability Statement

Data are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Cortis, K.; Davis, B. Over a decade of social opinion mining: A systematic review. Artif. Intell. Rev. 2021, 54, 4873–4965. [Google Scholar] [PubMed]
  2. Basile, V.; Cauteruccio, F.; Terracina, G. How dramatic events can affect emotionality in social posting: The impact of COVID-19 on Reddit. Future Internet 2021, 13, 29. [Google Scholar] [CrossRef]
  3. Lai, M.; Tambuscio, M.; Patti, V.; Ruffo, G.; Rosso, P. Stance polarity in political debates: A diachronic perspective of network homophily and conversations on Twitter. Data Knowl. Eng. 2019, 124, 101738. [Google Scholar]
  4. Ramachandran, D.; Parvathi, R. A novel domain and event adaptive tweet augmentation approach for enhancing the classification of crisis related tweets. Data Knowl. Eng. 2021, 135, 101913. [Google Scholar]
  5. Liu, H.; Liu, W.; Yoganathan, V.; Osburg, V.-S. COVID-19 information overload and generation Z’s social media discontinuance intention during the pandemic lockdown. Technol. Forecast. Soc. Change 2021, 166, 120600. [Google Scholar] [PubMed]
  6. Zhuang, W.; Zeng, Q.; Zhang, Y.; Liu, C.; Fan, W. What makes user-generated content more helpful on social media platforms? Insights from creator interactivity perspective. Inf. Process. Manag. 2023, 60, 103201. [Google Scholar]
  7. Ribeiro-Navarrete, S.; Saura, J.R.; Palacios-Marqués, D. Towards a new era of mass data collection: Assessing pandemic surveillance technologies to preserve user privacy. Technol. Forecast. Soc. Change 2021, 167, 120681. [Google Scholar]
  8. Yang, S.; Du, Q.; Zhu, G.; Cao, J.; Chen, L.; Qin, W.; Wang, Y. Balanced influence maximization in social networks based on deep reinforcement learning. Neural Netw. 2023. [Google Scholar] [CrossRef]
  9. Tandon, C.; Revankar, S.; Parihar, S.S. How can we predict the impact of the social media messages on the value of cryptocurrency? Insights from big data analytics. Int. J. Inf. Manag. Data Insights 2021, 1, 100035. [Google Scholar]
  10. Li, W.; Guo, C.; Deng, Z.; Liu, F.; Wang, J.; Guo, R.; Wang, C.; Jin, Q. Coevolution modeling of group behavior and opinion based on public opinion perception. Knowl.-Based Syst. 2023, 270, 110547. [Google Scholar]
  11. Wang, Y.; Cao, J.; Bu, Z.; Wu, J.; Wang, Y. Dual Structural Consistency Preserving Community Detection on Social Networks. IEEE Trans. Knowl. Data Eng. 2023. [Google Scholar] [CrossRef]
  12. Roma, P.; Aloini, D. How does brand-related user-generated content differ across social media? Evidence reloaded. J. Bus. Res. 2019, 96, 322–339. [Google Scholar] [CrossRef]
  13. Sreejesh, S.; Paul, J.; Strong, C.; Pius, J. Consumer response towards social media advertising: Effect of media interactivity, its conditions and the underlying mechanism. Int. J. Inf. Manag. 2020, 54, 102155. [Google Scholar]
  14. Kang, K.; Lu, J.; Guo, L.; Li, W. The dynamic effect of interactivity on customer engagement behavior through tie strength: Evidence from live streaming commerce platforms. Int. J. Inf. Manag. 2021, 56, 102251. [Google Scholar] [CrossRef]
  15. Krumm, J.; Davies, N.; Narayanaswami, C. User-generated content. IEEE Pervasive Comput. 2008, 7, 10–11. [Google Scholar] [CrossRef]
  16. Zailskaite-Jakste, L.; Minelgaite, I. Consumer engagement behavior perspective in social media: Mediating role and impact on brand equity. J. East. Eur. Cent. Asian Res. (JEECAR) 2021, 8, 160–170. [Google Scholar] [CrossRef]
  17. Chen, Y.; Xie, J. Online consumer review: Word-of-mouth as a new element of marketing communication mix. Manag. Sci. 2008, 54, 477–491. [Google Scholar] [CrossRef]
  18. Chevalier, J.A.; Mayzlin, D. The effect of word of mouth on sales: Online book reviews. J. Mark. Res. 2006, 43, 345–354. [Google Scholar] [CrossRef]
  19. Dellarocas, C. The digitization of word of mouth: Promise and challenges of online feedback mechanisms. Manag. Sci. 2003, 49, 1407–1424. [Google Scholar] [CrossRef]
  20. Pu, J.; Chen, Y.; Qiu, L.; Cheng, H.K. Does identity disclosure help or hurt user content generation? Social presence, inhibition, and displacement effects. Inf. Syst. Res. 2020, 31, 297–322. [Google Scholar] [CrossRef]
  21. Santos, M.L.B.d. The “so-called” UGC: An updated definition of user-generated content in the age of social media. Online Inf. Rev. 2022, 46, 95–113. [Google Scholar] [CrossRef]
  22. Li, S.; Liu, F.; Zhang, Y.; Zhu, B.; Zhu, H.; Yu, Z. Text Mining of User-Generated Content (UGC) for Business Applications in E-Commerce: A Systematic Review. Mathematics 2022, 10, 3554. [Google Scholar] [CrossRef]
  23. Aras, A.; Xu, X.; Peñaloza, L. Deciphering B2B marketers’ concerns in marketing ‘with’clients: Further insights into how B2B characteristics foster and inhibit UGC generation and its leverage. Ind. Mark. Manag. 2022, 101, 71–81. [Google Scholar] [CrossRef]
  24. Li, Y.; Cao, J.; Zhang, N.; Sha, K. Identification and evaluation of competitive products based on online user-generated content. Expert Syst. Appl. 2023, 225, 120168. [Google Scholar]
  25. Liu, X.; Lin, S.; Jiang, S.; Chen, M.; Li, J. Social support acquisition in online health communities: A social capital perspective. Internet Res. 2023, 33, 664–695. [Google Scholar] [CrossRef]
  26. Kankanhalli, A.; Tan, B.C.; Wei, K.-K. Contributing knowledge to electronic knowledge repositories: An empirical investigation. MIS Q. 2005, 113–143. [Google Scholar] [CrossRef]
  27. Ma, M.; Agarwal, R. Through a glass darkly: Information technology design, identity verification, and knowledge contribution in online communities. Inf. Syst. Res. 2007, 18, 42–67. [Google Scholar] [CrossRef]
  28. Wasko, M.M.; Faraj, S. Why should I share? Examining social capital and knowledge contribution in electronic networks of practice. MIS Q. 2005, 35–57. [Google Scholar] [CrossRef]
  29. Romero-Rodriguez, L.M.; Castillo-Abdul, B. Toward state-of-the-art on social marketing research in user-generated content (UGC) and influencers. J. Manag. Dev. 2023. [Google Scholar] [CrossRef]
  30. Zhang, X.; Zhu, F. Group size and incentives to contribute: A natural experiment at Chinese Wikipedia. Am. Econ. Rev. 2011, 101, 1601–1615. [Google Scholar] [CrossRef]
  31. Cabral, L.; Li, L. A dollar for your thoughts: Feedback-conditional rebates on eBay. Manag. Sci. 2015, 61, 2052–2063. [Google Scholar] [CrossRef]
  32. Geng, R.; Chen, X. Privilege or equality? A natural experiment with content monetization in social media. Inf. Manag. 2022, 59, 103715. [Google Scholar] [CrossRef]
  33. Burtch, G.; He, Q.; Hong, Y.; Lee, D. How do peer awards motivate creative content? Experimental evidence from Reddit. Manag. Sci. 2022, 68, 3488–3506. [Google Scholar] [CrossRef]
  34. Cao, X.; Luo, Z.; Qiu, J.; Liu, Y. Does ostracism impede Chinese tourist self-disclosure on WeChat? The perspective of social anxiety and self-construal. J. Hosp. Tour. Manag. 2022, 50, 178–187. [Google Scholar] [CrossRef]
  35. Godes, D.; Silva, J.C. Sequential and temporal dynamics of online opinion. Mark. Sci. 2012, 31, 448–473. [Google Scholar] [CrossRef]
  36. Goes, P.B.; Lin, M.; Au Yeung, C.-M. “Popularity effect” in user-generated content: Evidence from online product reviews. Inf. Syst. Res. 2014, 25, 222–238. [Google Scholar] [CrossRef]
  37. Chen, Y.; Wang, Q.; Xie, J. Online social interactions: A natural experiment on word of mouth versus observational learning. J. Mark. Res. 2011, 48, 238–254. [Google Scholar] [CrossRef]
  38. Goh, K.-Y.; Heng, C.-S.; Lin, Z. Social media brand community and consumer behavior: Quantifying the relative impact of user-and marketer-generated content. Inf. Syst. Res. 2013, 24, 88–107. [Google Scholar] [CrossRef]
  39. Lin, Z. An empirical investigation of user and system recommendations in e-commerce. Decis. Support Syst. 2014, 68, 111–124. [Google Scholar] [CrossRef]
  40. Pavlou, P.A.; Dimoka, A. The nature and role of feedback text comments in online marketplaces: Implications for trust building, price premiums, and seller differentiation. Inf. Syst. Res. 2006, 17, 392–414. [Google Scholar] [CrossRef]
  41. Dhar, V.; Chang, E.A. Does chatter matter? The impact of user-generated content on music sales. J. Interact. Mark. 2009, 23, 300–307. [Google Scholar] [CrossRef]
  42. Tang, T.; Fang, E.; Wang, F. Is neutral really neutral? The effects of neutral user-generated content on product sales. J. Mark. 2014, 78, 41–58. [Google Scholar] [CrossRef]
  43. Kwark, Y.; Chen, J.; Raghunathan, S. Online product reviews: Implications for retailers and competing manufacturers. Inf. Syst. Res. 2014, 25, 93–110. [Google Scholar] [CrossRef]
  44. Tirunillai, S.; Tellis, G.J. Does chatter really matter? Dynamics of user-generated content and stock performance. Mark. Sci. 2012, 31, 198–215. [Google Scholar] [CrossRef]
  45. Ozuem, W.; Willis, M.; Howell, K.; Ranfagni, S.; Rovai, S. Examining user-generated content, service failure recovery and customer–brand relationships: An exploration through commitment-trust theory. Internet Res. 2023; ahead of print. [Google Scholar]
  46. Hou, L.; Guan, L.; Zhou, Y.; Shen, A.; Wang, W.; Luo, A.; Lu, H.; Zhu, J.J. Staying, switching, and multiplatforming of user-generated content activities: A 12-year panel study. Internet Res. 2023, 33, 1372–1398. [Google Scholar] [CrossRef]
  47. Jiang, Y.; Ho, Y.-C.; Yan, X.; Tan, Y. What’s in a “username”? The effect of perceived anonymity on herding in crowdfunding. Inf. Syst. Res. 2022, 33, 17. [Google Scholar] [CrossRef]
  48. Lei, Z.; Yin, D.; Zhang, H. Focus within or on others: The impact of reviewers’ attentional focus on review helpfulness. Inf. Syst. Res. 2021, 32, 801–819. [Google Scholar] [CrossRef]
  49. Adams, R.; Jeanrenaud, S.; Bessant, J.; Denyer, D.; Overy, P. Sustainability-oriented innovation: A systematic review. Int. J. Manag. Rev. 2016, 18, 180–205. [Google Scholar] [CrossRef]
  50. Georgiev, G.V.; Nanjappan, V. Sustainability Considerations in Digital Fabrication Design Education. Sustainability 2023, 15, 1519. [Google Scholar] [CrossRef]
  51. Vansteenkiste, M.; Niemiec, C.P.; Soenens, B. The development of the five mini-theories of self-determination theory: An historical overview, emerging trends, and future directions. Decade Ahead: Theor. Perspect. Motiv. Achiev. 2010, 16, 105–165. [Google Scholar]
  52. Lübbecke, S.; Schnedler, W. Don’t patronize me! An experiment on preferences for authorship. J. Econ. Manag. Strategy 2020, 29, 420–438. [Google Scholar] [CrossRef]
  53. Zuckerman, M.; Porac, J.; Lathin, D.; Deci, E.L. On the importance of self-determination for intrinsically-motivated behavior. Personal. Soc. Psychol. Bull. 1978, 4, 443–446. [Google Scholar] [CrossRef]
  54. Wang, T.; Zheng, X.; Liang, J.; An, K.; He, Y.; Nuo, M.; Wang, W.; Lei, J. Use of machine learning to mine user-generated content from mobile health apps for weight loss to assess factors correlated with user satisfaction. JAMA Netw. Open 2022, 5, e2215014. [Google Scholar] [CrossRef] [PubMed]
  55. White, R.W. Motivation reconsidered: The concept of competence. Psychol. Rev. 1959, 66, 297. [Google Scholar] [CrossRef] [PubMed]
  56. Zeng, Z.; Dai, H.; Zhang, D.J.; Zhang, H.; Zhang, R.; Xu, Z.; Shen, Z.-J.M. The impact of social nudges on user-generated content for social network platforms. Manag. Sci. 2023, 69, 5189–5208. [Google Scholar] [CrossRef]
  57. Ryan, R.M.; Deci, E.L. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am. Psychol. 2000, 55, 68. [Google Scholar] [CrossRef]
  58. Gu, Y.; Zhu, J. The effect of online review and interaction on value co-creation in tourism virtual community. Cogent Soc. Sci. 2023, 9, 2261234. [Google Scholar] [CrossRef]
  59. Medeiros, M.; Ozturk, A.; Hancer, M.; Weinland, J.; Okumus, B. Understanding travel tracking mobile application usage: An integration of self determination theory and UTAUT2. Tour. Manag. Perspect. 2022, 42, 100949. [Google Scholar] [CrossRef]
  60. Ma, Z.; Gu, B. The influence of firm-Generated video on user-Generated video: Evidence from China. Int. J. Eng. Bus. Manag. 2022, 14, 18479790221118628. [Google Scholar] [CrossRef]
  61. Demircioglu, M.A.; Chen, C.-A. Public employees’ use of social media: Its impact on need satisfaction and intrinsic work motivation. Gov. Inf. Q. 2019, 36, 51–60. [Google Scholar] [CrossRef]
  62. Deci, E.L.; Ryan, R.M. Facilitating optimal motivation and psychological well-being across life’s domains. Can. Psychol. Psychol. Can. 2008, 49, 14. [Google Scholar] [CrossRef]
  63. Coun, M.J.; Peters, P.; Blomme, R.J. ‘Let’s share!’The mediating role of employees’ self-determination in the relationship between transformational and shared leadership and perceived knowledge sharing among peers. Eur. Manag. J. 2019, 37, 481–491. [Google Scholar] [CrossRef]
  64. Nadzri, W.N.M.; Hashim, A.J.C.; Majid, M.; Jalil, N.A.A.; Alzoubi, H.M.; Alshurideh, M.T. Share Your Beautiful Journey: Investigating User Generated Content (UGC) and Webrooming Among Malaysian Online Shoppers. In The Effect of Information Technology on Business and Marketing Intelligence Systems; Springer: Berlin/Heidelberg, Germany, 2023; pp. 2265–2285. [Google Scholar]
  65. Bélanger-Gravel, A.; Gauvin, L.; Fuller, D.; Drouin, L. Association of implementation of a public bicycle share program with intention and self-efficacy: The moderating role of socioeconomic status. J. Health Psychol. 2016, 21, 944–953. [Google Scholar] [CrossRef] [PubMed]
  66. Zhao, L.; Lu, Y.; Wang, B.; Chau, P.Y.; Zhang, L. Cultivating the sense of belonging and motivating user participation in virtual communities: A social capital perspective. Int. J. Inf. Manag. 2012, 32, 574–588. [Google Scholar] [CrossRef]
  67. Liang, T.-P.; Ho, Y.-T.; Li, Y.-W.; Turban, E. What drives social commerce: The role of social support and relationship quality. Int. J. Electron. Commer. 2011, 16, 69–90. [Google Scholar] [CrossRef]
  68. Mikulincer, M.E.; Shaver, P.R.; Cooper, M.; Larsen, R.J. APA handbook of personality and social psychology, Volume 4: Personality processes and individual differences; American Psychological Association: Washington, DC, USA, 2015. [Google Scholar]
  69. Coursaris, C.K.; Liu, M. An analysis of social support exchanges in online HIV/AIDS self-help groups. Comput. Hum. Behav. 2009, 25, 911–918. [Google Scholar] [CrossRef]
  70. Colineau, N.; Paris, C. Talking about your health to strangers: Understanding the use of online social networks by patients. New Rev. Hypermedia Multimed. 2010, 16, 141–160. [Google Scholar] [CrossRef]
  71. Antheunis, M.L.; Valkenburg, P.M.; Peter, J. Getting acquainted through social network sites: Testing a model of online uncertainty reduction and social attraction. Comput. Hum. Behav. 2010, 26, 100–109. [Google Scholar] [CrossRef]
  72. Forest, A.L.; Wood, J.V. When social networking is not working: Individuals with low self-esteem recognize but do not reap the benefits of self-disclosure on Facebook. Psychol. Sci. 2012, 23, 295–302. [Google Scholar] [CrossRef]
  73. Park, N.; Jin, B.; Jin, S.-A.A. Effects of self-disclosure on relational intimacy in Facebook. Comput. Hum. Behav. 2011, 27, 1974–1983. [Google Scholar] [CrossRef]
  74. Zhu, D.H.; Sun, H.; Chang, Y.P. Effect of social support on customer satisfaction and citizenship behavior in online brand communities: The moderating role of support source. J. Retail. Consum. Serv. 2016, 31, 287–293. [Google Scholar] [CrossRef]
  75. O’Connor, E.L.; Longman, H.; White, K.M.; Obst, P.L. Sense of community, social identity and social support among players of massively multiplayer online games (MMOGs): A qualitative analysis. J. Community Appl. Soc. Psychol. 2015, 25, 459–473. [Google Scholar] [CrossRef]
  76. Trepte, S.; Reinecke, L.; Juechems, K. The social side of gaming: How playing online computer games creates online and offline social support. Comput. Hum. Behav. 2012, 28, 832–839. [Google Scholar] [CrossRef]
  77. Chatman, J.A. Matching people and organizations: Selection and socialization in public accounting firms. In Proceedings of the Academy of Management Proceedings; Academy of Management: Briarcliff Manor, NY, USA, 1989; pp. 199–203. [Google Scholar]
  78. Kristof, A.L. Person-organization fit: An integrative review of its conceptualizations, measurement, and implications. Pers. Psychol. 1996, 49, 1–49. [Google Scholar] [CrossRef]
  79. Tsvetovat, M.; Kouznetsov, A. Social Network Analysis for Startups: Finding Connections on the Social Web; O’Reilly Media, Inc.: Newton, MA, USA, 2011. [Google Scholar]
  80. Hsu, T.W.; Niiya, Y.; Thelwall, M.; Ko, M.; Knutson, B.; Tsai, J.L. Social media users produce more affect that supports cultural values, but are more influenced by affect that violates cultural values. J. Personal. Soc. Psychol. 2021, 121, 969. [Google Scholar] [CrossRef] [PubMed]
  81. Baek, E.; Scholz, C.; O’Donnell, M.; Falk, E. Neural correlates of selecting and sharing information. Int. Commun. Assoc. Fukuoka Jpn. 2016. [Google Scholar]
  82. Bhattacherjee, A. An empirical analysis of the antecedents of electronic commerce service continuance. Decis. Support Syst. 2001, 32, 201–214. [Google Scholar] [CrossRef]
  83. Hobfoll, S.E.; Shoham, S.B.; Ritter, C. Women’s satisfaction with social support and their receipt of aid. J. Personal. Soc. Psychol. 1991, 61, 332. [Google Scholar] [CrossRef]
  84. Laurenceau, J.-P.; Barrett, L.F.; Pietromonaco, P.R. Intimacy as an interpersonal process: The importance of self-disclosure, partner disclosure, and perceived partner responsiveness in interpersonal exchanges. J. Personal. Soc. Psychol. 1998, 74, 1238. [Google Scholar] [CrossRef]
  85. Chen, C.-C.; Lin, Y.-C. What drives live-stream usage intention? The perspectives of flow, entertainment, social interaction, and endorsement. Telemat. Inform. 2018, 35, 293–303. [Google Scholar] [CrossRef]
  86. Jang, W.E.; Chun, J.W.; Kim, J.J.; Bucy, E. Effects of self-presentation strategy and tie strength on Facebook users’ happiness and subjective vitality. J. Happiness Stud. 2021, 1–19. [Google Scholar] [CrossRef]
  87. Cable, D.M.; Judge, T.A. Interviewers’ perceptions of person–organization fit and organizational selection decisions. J. Appl. Psychol. 1997, 82, 546. [Google Scholar] [CrossRef] [PubMed]
  88. Rindfleisch, A.; Moorman, C. The acquisition and utilization of information in new product alliances: A strength-of-ties perspective. J. Mark. 2001, 65, 1–18. [Google Scholar] [CrossRef]
  89. Dahl, D.W.; Moreau, C.P. Thinking inside the box: Why consumers enjoy constrained creative experiences. J. Mark. Res. 2007, 44, 357–369. [Google Scholar] [CrossRef]
  90. Morhart, F.M.; Herzog, W.; Tomczak, T. Brand-specific leadership: Turning employees into brand champions. J. Mark. 2009, 73, 122–142. [Google Scholar] [CrossRef]
  91. Bandura, A. Self-efficacy mechanism in human agency. Am. Psychol. 1982, 37, 122. [Google Scholar] [CrossRef]
  92. Nambisan, S.; Baron, R.A. Virtual customer environments: Testing a model of voluntary participation in value co-creation activities. J. Prod. Innov. Manag. 2009, 26, 388–406. [Google Scholar] [CrossRef]
  93. Lien, C.-H.; Cao, Y.; Zhou, X. Service quality, satisfaction, stickiness, and usage intentions: An exploratory evaluation in the context of WeChat services. Comput. Hum. Behav. 2017, 68, 403–410. [Google Scholar] [CrossRef]
  94. Chin, W.W. The partial least squares approach to structural equation modeling. Mod. Methods Bus. Res. 1998, 295, 295–336. [Google Scholar]
  95. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  96. Schumacker, R.E.; Lomax, R.G. A Beginner’s Guide to Structural Equation Modeling; Psychology Press: London, UK, 2004. [Google Scholar]
  97. Kline, R.B. Principles and Practice of Structural Equation Modeling; Guilford publications: New York, NY, USA, 2023. [Google Scholar]
  98. Browne, M.W.; Cudeck, R. Alternative ways of assessing model fit. Sociol. Methods Res. 1992, 21, 230–258. [Google Scholar] [CrossRef]
  99. Hair, J.F. Multivariate Data Analysis; Kennesaw State University: Kennesaw, GA, USA, 2009. [Google Scholar]
  100. Kelley, T.; Kelley, D. Creative Confidence: Unleashing the Creative Potential within Us All; Currency: Sydney, Australia, 2013. [Google Scholar]
  101. Ryan, R.M.; Deci, E.L. Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemp. Educ. Psychol. 2020, 61, 101860. [Google Scholar] [CrossRef]
  102. Ellison, N.B.; Steinfield, C.; Lampe, C. The benefits of Facebook “friends:” Social capital and college students’ use of online social network sites. J. Comput.-Mediat. Commun. 2007, 12, 1143–1168. [Google Scholar] [CrossRef]
  103. Reinecke, L.; Trepte, S. Authenticity and well-being on social network sites: A two-wave longitudinal study on the effects of online authenticity and the positivity bias in SNS communication. Comput. Hum. Behav. 2014, 30, 95–102. [Google Scholar] [CrossRef]
  104. Norusis, M.J. SPSS for Windows: Base System User’s guide, Release 5.0; SPSS Incorporated: Chicago, IL, USA, 1992. [Google Scholar]
  105. Kristof-Brown, A.L. Perceived applicant fit: Distinguishing between recruiters’perceptions of person-job and person-organization fit. Pers. Psychol. 2000, 53, 643–671. [Google Scholar] [CrossRef]
  106. Jozani, M.; Ayaburi, E.; Ko, M.; Choo, K.-K.R. Privacy concerns and benefits of engagement with social media-enabled apps: A privacy calculus perspective. Comput. Hum. Behav. 2020, 107, 106260. [Google Scholar] [CrossRef]
  107. Jang, C.; Sung, W. Beyond the privacy paradox: The moderating effect of online privacy concerns on online service use behavior. Telemat. Inform. 2021, 65, 101715. [Google Scholar] [CrossRef]
  108. Tafesse, W.; Wien, A. Implementing social media marketing strategically: An empirical assessment. J. Mark. Manag. 2018, 34, 732–749. [Google Scholar] [CrossRef]
  109. Li, F.; Larimo, J.; Leonidou, L.C. Social media marketing strategy: Definition, conceptualization, taxonomy, validation, and future agenda. J. Acad. Mark. Sci. 2021, 49, 51–70. [Google Scholar] [CrossRef]
  110. Schmidt, S.; Eisend, M. Advertising repetition: A meta-analysis on effective frequency in advertising. J. Advert. 2015, 44, 415–428. [Google Scholar] [CrossRef]
  111. McLachlan, S.; Cohen, B. How Often to Post to Social Media in 2022. Hootsuite 2021, 16, 2021. [Google Scholar]
  112. Audrezet, A.; De Kerviler, G.; Moulard, J.G. Authenticity under threat: When social media influencers need to go beyond self-presentation. J. Bus. Res. 2020, 117, 557–569. [Google Scholar] [CrossRef]
  113. Tafesse, W.; Wood, B.P. Followers’ engagement with instagram influencers: The role of influencers’ content and engagement strategy. J. Retail. Consum. Serv. 2021, 58, 102303. [Google Scholar] [CrossRef]
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Figure 1. Hypothetical model of work-sharing intention of users in the designer UGC community.
Figure 1. Hypothetical model of work-sharing intention of users in the designer UGC community.
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Figure 2. Data analysis flow chart.
Figure 2. Data analysis flow chart.
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Figure 3. Results of path analysis.
Figure 3. Results of path analysis.
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Table 1. Constructs and definitions.
Table 1. Constructs and definitions.
ConstructDefinition
Self-Determination TheoryAutonomyNeed for autonomy refers to the desire to initiate one’s own actions
CompetenceNeed for competence refers the desire to achieve optimally challenging tasks
RelatednessNeed for relatedness refers to the desire to establish and maintain mutual care with others
Online Social SupportOnline social support is an internet-based form of social support
Value FitValue fit is a concept used to describe the similarity or consistency of values between individuals or groups
Tie StrengthTie strength refers to the intimacy or strength of the relationship between users
Work-Sharing IntentionWork-sharing intention refers to the willingness of creators or teams to share, disseminate or distribute their works, ideas or knowledge with others
Table 2. Measurement items for research variables of users’ work-sharing intention in the designer UGC community.
Table 2. Measurement items for research variables of users’ work-sharing intention in the designer UGC community.
ConstructItemsSource
Value FitVF1: I find that the values upheld by the designer UGC community align closely with my own.[87]
VF2: Within the designer UGC community, I can identify designers whose values resonate with mine for potential collaboration.
VF3: When engaging with the designer UGC community, I proactively seek out design works that reflect similar values.
Tie StrengthTS1: Within the designer UGC community, closely knit designers often recommend each other’s exemplary design pieces.[88]
TS2: The designer UGC community facilitates designers in exchanging insights, collaborating, and jointly addressing concerns.
TS3: I’m aware of users who have had fruitful collaborations within the designer UGC community and anticipate seeking new partnership opportunities there.
Online Social SupportOSS1: When discussing design works in the designer UGC community, other designers will show their interest.[67,74]
OSS2: When I share my perspectives on design in the designer UGC community, many designers often concur with my viewpoints.
OSS3: When I seek help within the designer UGC community, they are willing to listen.
AutonomyAU1: By sharing my works, I can present an authentic expression of myself to others.[89,90]
AU2: I can freely voice my opinions and insights about design works within the designer UGC community.
AU3: In the designer UGC community, I reserve the right to exclusively appreciate the genres of work or specific authors I favor.
CompetenceCO1: I believe I have amassed a wealth of knowledge, information, and experience about design within the designer UGC community.[89,91]
CO2: I am confident in my ability to offer valuable support and assistance to other designers.
CO3: I believe I can articulate my thoughts and perspectives on the value of design works clearly.
RelatednessRE1: I take great joy in sharing my design pieces with other designers.[92]
RE2: I genuinely enjoy and frequently engage in design discussions with other designers.
RE3: The designer UGC community offers a space for collective creation and interaction, providing a sense of relatedness in the design realm.
Work-Sharing IntentionWSI1: I plan to continually share my design works in the designer UGC community.[93]
WSI2: In the future, I intend to share my creations within the designer UGC community.
WSI3: I aim to gradually increase my sharing frequency within the designer UGC community.
Table 3. Demographic information.
Table 3. Demographic information.
CharacteristicsOptionsFrequencyRatio (%)
GenderMale11840.27
Female17559.73
AgeUnder 18 years old51.71
18–2523780.89
26–30289.56
31–40155.12
41–5082.73
EducationHigh school/technical secondary school144.78
College3210.92
Undergraduate course17359.04
Graduate or above7425.26
Design directionVisual communication design8328.33
Industrial design237.85
Product design4916.72
Environmental design6321.50
Fashion design124.10
Digital media art design4615.70
Public art design175.80
Table 4. Results of convergent validity.
Table 4. Results of convergent validity.
DimensionItemUnstandardized Factor LoadingStandardized Factor LoadingS.E.p-ValueAVECR
VFVF11.0000.891--0.7780.913
VF21.0340.8780.0480.000
VF30.9320.8770.0440.000
TSTS11.0000.911--0.7980.922
TS21.0350.9180.0390.000
TS31.0260.8490.0480.000
OSSOSS11.0000.921--0.8380.939
OSS21.0060.9440.0340.000
OSS30.9800.8790.0410.000
AUAU11.0000.933--0.8560.947
AU21.0210.9490.0320.000
AU30.9550.8940.0360.000
COCO11.0000.909--0.8280.935
CO21.0530.9100.0410.000
CO31.0620.9100.0420.000
RERE11.0000.929--0.8630.950
RE21.0600.9410.0340.000
RE31.0070.9170.0360.000
WSIWSI11.0000.911--0.8160.930
WSI20.9970.9120.0380.000
WSI30.9860.8870.0410.000
Note: represent significance levels of 1%, 5%, and 10%, respectively.
Table 5. Results of discriminant validity.
Table 5. Results of discriminant validity.
VFTSOSSAFCFRFWSI
VF0.882
TS0.8070.894
OSS0.7970.8830.915
AU0.8010.8730.8380.925
CO0.7620.8110.8330.8210.910
RE0.7640.8290.8330.8370.8770.929
WSI0.7660.8400.8140.8580.8580.8940.903
Note: The bolded values on the diagonal are the square root of AVE.
Table 6. Results of model fit test.
Table 6. Results of model fit test.
Indicesχ2/dfRMSEARMRCFINFINNFITLIIFISRMR
Recommended criteria<5<0.10<0.05>0.9>0.9>0.9>0.9>0.9<0.1
Results3.7730.0970.0420.9410.9220.9300.9300.9410.028
Table 7. Results of path analysis.
Table 7. Results of path analysis.
Relationship PathStandardized Regression WeightsS.E.pHypothesesResults
AU→WSI0.2690.0690H1Support
CO→0.1780.0670.009H2Support
RE→0.5180.0670H3Support
TS→0.0430.0900.624H13Not Support
OSS→CO0.8420.0870H5Support
VF→0.0910.0770.258H8Not Support
OSS→AU0.7170.0780H4Support
VF→0.2410.070.001H7Support
OSS→RE0.8540.0840H6Support
VF→0.0800.0750.305H9Not Support
OSS→TS0.8000.0740H11Support
VF→0.1780.0660.012H12Support
OSS→VF0.8750.0530H10Support
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Song, H.; Wei, J.; Jiang, Q. What Influences Users’ Intention to Share Works in Designer-Driven User-Generated Content Communities? A Study Based on Self-Determination Theory. Systems 2023, 11, 540. https://doi.org/10.3390/systems11110540

AMA Style

Song H, Wei J, Jiang Q. What Influences Users’ Intention to Share Works in Designer-Driven User-Generated Content Communities? A Study Based on Self-Determination Theory. Systems. 2023; 11(11):540. https://doi.org/10.3390/systems11110540

Chicago/Turabian Style

Song, Hongcai, Jie Wei, and Qianling Jiang. 2023. "What Influences Users’ Intention to Share Works in Designer-Driven User-Generated Content Communities? A Study Based on Self-Determination Theory" Systems 11, no. 11: 540. https://doi.org/10.3390/systems11110540

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